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Many organisms exhibit collecting and gathering behaviors as a foraging and survival method. Benthic macroinvertebrates are classified as collector–gatherers due to their collection of particulate matter. Among these, the aquatic oligochaete Lumbriculus variegatus (California blackworms) demonstrates the ability to ingest both organic and inorganic materials, including microplastics. However, earlier studies have only qualitatively described their collecting behaviors for such materials. The mechanism by which blackworms consolidate discrete particles into a larger clump remains unexplored quantitatively. In this study, we analyze a group of blackworms in a large arena with an aqueous algae solution (organic particles) and find that their relative collecting efficiency is proportional to population size. We found that doubling the population size (N = 25–N = 50) results in a decrease in time to reach consolidation by more than half. Microscopic examination of individual blackworms reveals that both algae and microplastics physically adhere to the worm’s body and form clumps due to external mucus secretions by the worms. Our observations also indicate that this clumping behavior reduces the worm’s exploration of its environment, possibly due to thigmotaxis. To validate these observed biophysical mechanisms, we create an active polymer model of a worm moving in a field of particulate debris. We simulate its adhesive nature by implementing a short-range attraction between the worm and the nearest surrounding particles. Our findings indicate an increase in gathering efficiency when we add an attractive force between particles, simulating the worm’s mucosal secretions. Our work provides a detailed understanding of the complex mechanisms underlying the collecting–gathering behavior in L. variegatus, informing the design of bioinspired synthetic collector systems, and advances our understanding of the ecological impacts of microplastics on benthic invertebrates.

 

Aggregation is a common behavior by which groups of organisms arrange into cohesive groups. Whether suspended in the air (like honey bee clusters), built on the ground (such as army ant bridges), or immersed in water (such as sludge worm blobs), these collectives serve a multitude of biological functions, from protection against predation to the ability to maintain a relatively desirable local environment despite a variable ambient environment. In this review, we survey dense aggregations of a variety of insects, other arthropods, and worms from a soft matter standpoint. An aggregation can be orders of magnitude larger than its individual organisms, consisting of tens to hundreds of thousands of individuals, and yet functions as a coherent entity. Understanding how aggregating organisms coordinate with one another to form a superorganism requires an interdisciplinary approach. We discuss how considering the physics of an aggregation can yield additional insights to those gained from ecological and physiological considerations, given that the aggregating individuals exchange information, energy, and matter continually with the environment and one another. While the connection between animal aggregations and the physics of non-living materials has been proposed since the early 1900s, the recent advent of physics of behavior studies provides new insights into social interactions governed by physical principles. Current efforts focus on eusocial insects; however, we show that these may just be the tip of an iceberg of superorganisms that take advantage of physical interactions and simple behavioral rules to adapt to changing environments. By bringing attention to a wide range of invertebrate aggregations, we wish to inspire a new generation of scientists to explore collective dynamics and bring a deeper understanding of the physics of dense living aggregations. 

As honeybees build their nests in preexisting tree cavities, they must deal with the presence of geometric constraints, resulting in nonregular hexagons and topological defects in the comb. In this work, we study how bees adapt to their environment in order to regulate the comb structure. Specifically, we identify the irregularities in honeycomb structure in the presence of various geometric frustrations. We 3D-print experimental frames with a variety of constraints imposed on the imprinted foundations. The combs constructed by the bees show clear evidence of recurring patterns in response to specific geometric frustrations on these starter frames. Furthermore, using an experimental-modeling framework, we demonstrate that these patterns can be successfully modeled and replicated through a simulated annealing process, in which the minimized potential is a variation of the Lennard-Jones potential that considers only first-neighbor interactions according to a Delaunay triangulation. Our simulation results not only confirm the connection between honeycomb structures and other crystal systems such as graphene, but also show that irregularities in the honeycomb structure can be explained as the result of analogous interactions between cells and their immediate surroundings, leading to emergent global order. Additionally, our computational model can be used as a first step to describe specific strategies that bees use to effectively solve geometric mismatches while minimizing cost of comb building. 

Honey bees (Apis mellifera L.) localize the queen and aggregate into a swarm by forming a collective scenting network to directionally propagate volatile pheromone signals. Previous experiments show the robustness of this communication strategy in the presence of physical obstacles that partially block pheromone flow and the path to the queen. Specifically, there is a delay in the formation of the scenting network and aggregation compared to a simple environment without perturbations. To better understand the effect of obstacles beyond temporal dynamics, we use the experimental results as inspiration to explore how the behavioral parameter space of collective scenting responds to obstacle. We extend an agent-based model previously developed for a simple environment to account for the presence of physical obstacles. We study how individual agents with simple behavioral rules for scenting and following concentration gradients can give rise to collective localization and swarming. We show that the bees are capable of navigating the more complex environment with a physical obstacle to localize the queen and aggregate around her, but their range of behavioral parameters is more limited and less flexible as a result of the spatial density heterogeneity in the bees imposed by the obstacle. 

Numerous worm and arthropod species form physically-connected aggregations in which interactions among individuals give rise to emergent macroscale dynamics and functionalities that enhance collective survival. In particular, some aquatic worms such as the California blackworm ( Lumbriculus variegatus ) entangle their bodies into dense blobs to shield themselves against external stressors and preserve moisture in dry conditions. Motivated by recent experiments revealing emergent locomotion in blackworm blobs, we investigate the collective worm dynamics by modeling each worm as a self-propelled Brownian polymer. Though our model is two-dimensional, compared to real three-dimensional worm blobs, we demonstrate how a simulated blob can collectively traverse temperature gradients via the coupling between the active motion and the environment. By performing a systematic parameter sweep over the strength of attractive forces between worms, and the magnitude of their directed self-propulsion, we obtain a rich phase diagram which reveals that effective collective locomotion emerges as a result of finely balancing a tradeoff between these two parameters. Our model brings the physics of active filaments into a new meso- and macroscale context and invites further theoretical investigation into the collective behavior of long, slender, semi-flexible organisms. 

Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow. 

To effectively forage in natural environments, organisms must adapt to changes in the quality and yield of food sources across multiple timescales. Individuals foraging in groups act based on both their private observations and the opinions of their neighbours. How do these information sources interact in changing environments? We address this problem in the context of honeybee colonies whose inhibitory social interactions promote adaptivity and consensus needed for effective foraging. Individual and social interactions within a mathematical model of collective decisions shape the nutrition yield of a group foraging from feeders with temporally switching quality. Social interactions improve foraging from a single feeder if temporal switching is fast or feeder quality is low. When the colony chooses from multiple feeders, the most beneficial form of social interaction is direct switching, whereby bees flip the opinion of nest-mates foraging at lower-yielding feeders. Model linearization shows that effective social interactions increase the fraction of the colony at the correct feeder (consensus) and the rate at which bees reach that feeder (adaptivity). Our mathematical framework allows us to compare a suite of social inhibition mechanisms, suggesting experimental protocols for revealing effective colony foraging strategies in dynamic environments.